At SCOR, we combine the Art and Science of risk to help build more resilient societies. Data and AI are at the heart of this ambition.
Within our Tech, Data & AI organization, we are seeking a Head of Data Platforms to lead the design, operation, and continuous evolution of SCOR’s enterprise data platform ecosystem, centered around Databricks and Palantir Foundry.
This role is for a senior leader who combines deep platform expertise, strong engineering leadership, and forward-looking innovation, especially in the agentic AI space. You will own the platforms that enable data, analytics, and AI at scale, ensuring they are trusted, governed, cost-efficient, and continuously scaling business growth and AI adoption.
You will play a critical role in shaping how SCOR leverages modern data platforms such as Databricks and Palantir Foundry (incl. AIP) to deliver high-impact business outcomes across underwriting, pricing, reserving, and corporate functions.
You will define and own the platform strategy, governance model, operating model, and engineering standards, ensuring that the platform enables scalable data products, AI use cases, and self-service analytics.
Beyond platform stability and delivery, you will drive innovation in the agentic AI space, enabling new ways of working through AI-powered workflows, automation, and decision intelligence. This role requires a strong balance between strategy, architecture, governance, and hands-on leadership, with a particular focus on:
- Architecture, governance, and compliance (data, AI, access, regulatory)
- Cost management (FinOps for data and AI platforms)
- Platform standards, adoption and best practices
- Enabling business-driven use cases at scale
Key duties and responsibilities
Under the responsibility of the Head of Data Platform, your mission will be to:
1. Platform Strategy & Ownership
- Define and execute the enterprise data platform vision, architecture, and roadmap across Databricks and Palantir Foundry, aligned with SCOR’s business and AI strategy
- Own the end-to-end platform architecture, including ingestion, transformation, storage, analytics, and AI enablement layers
- Ensure the data platform scales in line with business growth, increased data volumes, and expanding AI use cases, while maintaining performance, resilience, and cost efficiency
- Lead the modernization and consolidation of legacy data systems into a unified, scalable platform ecosystem
2. Platform Enablement & Adoption
- Build and lead a Data Platform Center of Excellence (CoE) to drive adoption, onboarding, and enablement
- Support business to empower self-service analytics and AI
- Act as a strategic partner to business and domain teams to prioritize, industrialize, and scale high-value use cases in line with platform capacity and business demand
3. Governance, Security & Compliance
- Establish and enforce enterprise-wide governance frameworks covering access control, and regulatory compliance
- Define and operate platform governance models
- Ensure alignment with regulatory requirements (e.g. data privacy, auditability, AI governance)
4. Cost Management & FinOps
- Own platform cost management (FinOps) across data and AI workloads, ensuring transparency, accountability, and optimization
- Implement cost control mechanisms
- Drive a culture where cost efficiency is embedded into engineering and data usage practices
5. Excellence & Best Practices
- Lead the definition of reference architectures, reusable patterns, and canonical datasets / data products
- Establish platform observability, monitoring, and operational excellence practices
6. Leadership & Stakeholder Management
- Lead and grow a high-performing global team (platform engineers, architects, governance, FinOps)
- Collaborate with executive stakeholders to align platform investments with business priorities.
- Act as the enterprise authority on data platforms, advising on strategy, innovation, and risk.
Required experience & competencies
- +8 years in platform management or architecture with management experience
- Proven experience leading enterprise data platforms at scale
- Strong experience with Databricks and/or Palantir Foundry (incl. governance, platform ops, and architecture)
- Experience in financial services, insurance, or other regulated industries is a strong plus
Required Education:
- BSc in computer or data science, software or computer engineering, applied math, physics, statistics, or a related field or equivalent experience
Technical Skills:
- Deep expertise in modern data platforms (Databricks Lakehouse, Palantir Foundry, cloud ecosystems)
- Strong knowledge of distributed data processing (Spark, PySpark, SQL)
- Expertise in data governance, metadata management, lineage, and data modeling
- Experience with AI/ML platforms and AI-driven architecture
- Proven ability to define, implement, and enforce enterprise-wide data platform standards, ensuring consistent engineering practices, data models, governance policies, and scalable usage across the organization
- Strong understanding of FinOps principles and cost optimization for data platforms
- Experience with CI/CD, DevOps, and infrastructure-as-code
- Familiarity with APIs, integration patterns, and data contracts
Behavioral & Management Skills:
- Proven experience scaling data platforms in line with business growth, increasing data volumes, and expanding AI workloads, while continuously optimizing performance, cost efficiency, and resource utilization
- Strategic thinker with strong execution capability
- Entrepreneurial mindset and strong sense of ownership
- Ability to balance innovation (AI) with governance and risk management
- Excellent stakeholder management at executive level
- Strong leadership and people development capabilities
- Ability to drive cultural transformation toward data and AI adoption
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